Safe, effective therapies generally target specific disease-related molecules that appear only in disease-related cell types. The problem: Gaps in our knowledge include a comprehensive definition of cell types in any vertebrate species over developmental time and knowledge of which genes each cell type expresses at what levels. Genes currently known only by sequence might provide unique targets for cell therapies if we knew which cell types express them. A way forward: It has recently become possible to identify the transcriptional profile of individual, single cells with unprecedented molecular precision using single-cell RNA sequencing (scRNA-seq) coupled with powerful highly dimensional-reducing software that groups cells into bioinformatically identified clusters containing cell types with closely related gene expression profiles. The goals of this project are first, the comprehensive identification of transcriptionally unique cell types over developmental time in zebrafish, a major medical model, and second, the release of these data as a resource to the research community in a convenient searchable format through the Zebrafish Information Network (ZFIN). Approach: Aim 1 is to define single cell transcriptome phenotypes for various stages of wild-type zebrafish embryos, larvae, and juveniles and to locate these annotated cell types by in situ hybridization experiments displaying the expression of cell type-specific marker genes on whole mounts and histological sections. Aim 2 is to define the single cell transcriptome phenotype for all major organs in wild-type zebrafish adult males and females and to identify prominent cell types in vivo by in situ hybridization for cell type-specific marker genes on histological sections. Aim 3 is to develop an automated bioinformatic pipeline to identify cell types in scRNA-seq clusters by comparing gene expression profiles to existing resources, including ZFIN, other model organism databases (AGR, Alliance of Genome Resources), and human gene expression data. Aim 4 is to develop an interface in ZFIN to enable the research community to easily query zebrafish scRNA- seq data. Innovation: No animal species currently has a comprehensive compendium of cell types organized by gene expression patterns on a genome-wide scale during development. Significance: This R24 application will develop resources and related materials that will 1) enhance, further characterize, and improve a critical animal model for the investigation of human disease mechanisms; 2) facilitate access to data generated from the use of animal models of human disease; and 3) address the research interests of many categorical NIH Institutes and Centers that focus on various organ systems and disease types. This resource will identify previously unknown cell types, thus facilitating the precision targeting of cell types for potential therapies; will associate previously unknown genes with specific cell types, thus increasing potential molecular targets for drug therapies; and will suggest hypotheses for gene expression networks, thus improving our knowledge of cellular mechanisms in health and deepening our understanding of gene interaction webs in disease etiology.